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An NF-?B signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells.

ABSTRACT: Background:While recent genome-wide association studies have suggested novel low-grade glioma (LGG) stratification models based on a molecular classification, we explored the potential clinical utility of patient-derived cells. Specifically, we assayed glioma-associated stem cells (GASC) that are patient-derived and representative of the glioma microenvironment. Methods:By next-generation sequencing, we analyzed the transcriptional profile of GASC derived from patients who underwent anaplastic transformation either within 48 months (GASC-BAD) or ?7 years (GASC-GOOD) after surgery. Gene set enrichment and pathway enrichment analyses were applied. The prognostic role of a nuclear factor-kappaB (NF-?B) signature derived from GASC-BAD was tested in 530 newly diagnosed diffuse LGG patients comprised within The Cancer Genome Atlas (TCGA) database. The prognostic value of the GASC upstream regulator p65 NF-?B was assessed, by univariate and multivariate Cox analyses, in a single center case study, including 146 grade II LGGs. Results:The key elements differentiating the transcriptome of GASC isolated from LGG with different prognoses were mostly related to hallmarks of cancer (eg, inflammatory/immune process, NF-?B activation). Consistently, the NF-?B signature extrapolated from the GASC study was prognostic in the dataset of TCGA. Finally, the nuclear expression of the NF-kB-p65 protein, assessed using an inexpensive immunohistochemical method, was an independent predictor of both overall survival and malignant progression-free survival in 146 grade II LGGs. Conclusion:This study demonstrates for the first time the independent prognostic role of NF-kB activation in LGG and outlines the role of patient-based stem cell models as a tool for precision medicine approaches.

SUBMITTER: Ius T

PROVIDER: S-EPMC5961156 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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An NF-κB signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells.

Ius Tamara T Ciani Yari Y Ruaro Maria Elisabetta ME Isola Miriam M Sorrentino Marisa M Bulfoni Michela M Candotti Veronica V Correcig Cecilia C Bourkoula Evgenia E Manini Ivana I Pegolo Enrico E Mangoni Damiano D Marzinotto Stefania S Radovic Slobodanka S Toffoletto Barbara B Caponnetto Federica F Zanello Andrea A Mariuzzi Laura L Di Loreto Carla C Beltrami Antonio Paolo AP Piazza Silvano S Skrap Miran M Cesselli Daniela D

Neuro-oncology 20180501 6

<h4>Background</h4>While recent genome-wide association studies have suggested novel low-grade glioma (LGG) stratification models based on a molecular classification, we explored the potential clinical utility of patient-derived cells. Specifically, we assayed glioma-associated stem cells (GASC) that are patient-derived and representative of the glioma microenvironment.<h4>Methods</h4>By next-generation sequencing, we analyzed the transcriptional profile of GASC derived from patients who underwe ...[more]

PMID: 29228370

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Project description:Background: Glioma is the most common primary tumor of the central nervous system. The conventional glioma treatment strategies include surgical excision and chemo- and radiation-therapy. Interferon Gamma (IFN-γ) is a soluble dimer cytokine involved in immune escape of gliomas. In this study, we sought to identify IFN-γ-related genes to construct a glioma prognostic model to guide its clinical treatment. Methods: RNA sequences and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the China Glioma Genome Atlas (CGGA). Using univariate Cox analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, IFN-γ-related prognostic genes were selected to construct a risk scoring model, and analyze its correlation with the clinical features. A high-precision nomogram was drawn to predict prognosis, and its performance was evaluated using calibration curve. Finally, immune cell infiltration and immune checkpoint molecule expression were analyzed to explore the tumor microenvironment characteristics associated with the risk scoring model. Results: Four out of 198 IFN-γ-related genes were selected to construct a risk score model with good predictive performance. The expression of four IFN-γ-related genes in glioma tissues was significantly increased compared to normal brain tissue (p < 0.001). Based on ROC analysis, the risk score model accurately predicted the overall survival rate of glioma patients at 1 year (AUC: The Cancer Genome Atlas 0.89, CGGA 0.59), 3 years (AUC: TCGA 0.89, CGGA 0.68), and 5 years (AUC: TCGA 0.88, CGGA 0.70). Kaplan-Meier analysis showed that the overall survival rate of the high-risk group was significantly lower than that of the low-risk group (p < 0.0001). Moreover, high-risk scores were associated with wild-type IDH1, wild-type ATRX, and 1P/19Q non-co-deletion. The nomogram predicted the survival rate of glioma patients based on the risk score and multiple clinicopathological factors such as age, sex, pathological grade, and IDH Status, among others. Risk score and infiltrating immune cells including CD8 T-cell, resting CD4 memory T-cell, regulatory T-cell (Tregs), M2 macrophages, resting NK cells, activated mast cells, and neutrophils were positively correlated (p < 0.05). In addition, risk scores closely associated with expression of immune checkpoint molecules such as PD-1, PD-L1, CTLA-4, LAG-3, TIM-3, TIGIT, CD48, CD226, and CD96. Conclusion: Our risk score model reveals that IFN-γ -associated genes are an independent prognostic factor for predicting overall survival in glioma, which is closely associated with immune cell infiltration and immune checkpoint molecule expression. This model will be helpful in predicting the effectiveness of immunotherapy and survival rate in patients with glioma.

Dataset Information

An NF-?B signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells.

Publications

An NF-κB signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells.

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